1. Field:
The present disclosure relates generally to image processing and, in particular, to reducing blur in an image. Still more particularly, the present disclosure relates to a method and apparatus for reducing the blur in an image using an artificial reference point object generated by an inertially stabilized source.
2. Background:
In some situations, images of a scene generated by an imaging system may be blurred. For example, objects in the scene may appear blurred in the images. At least a portion of the blurring of objects in an image may occur in response to relative movement between the imaging system and the scene. This type of blur may be referred to as motion blur. Currently, a number of techniques is available for reducing the blur and, in particular, the motion blur, in an image or a sequence of images. Reducing the blur in an image may be referred to as “deblurring” the image.
Currently available deblurring techniques are typically grouped into two categories, pre-processing techniques and post-processing techniques. Pre-processing techniques are implemented before an image has been generated. Pre-processing techniques typically comprise hardware-based methods, which may increase the cost for deblurring images more than desired.
Post-processing techniques are implemented after an image has been generated. Some post-processing techniques use deconvolution algorithms to reduce the blur in an image. These deconvolution algorithms rely on a point spread function (PSF) for the image to reduce the blur in the image.
The point spread function for an image is the response of the imaging system that generated the image to a point object. In other words, the point spread function estimates the amount of blur for a point object in an image. The point spread function may be independent of a position of the point object within a field of view of the imaging system. In some cases, with high resolution narrow field of view imaging systems, the point spread function may have a high degree of accuracy independent of the position of the point object within the field of view or over a particular region of the image.
The image generated by an imaging system may be considered a convolution of a “pristine image” and the point spread function for the image. As used herein, a “pristine image” is the image without any blur. Currently available deconvolution algorithms use a point spread function identified for the image based on optical properties of the imaging system that generated the image. These deconvolution algorithms deconvolve the image generated by the imaging system with this point spread function to obtain a modified image with reduced blur relative to the image generated by the imaging system.
However, in some cases, the point spread function identified for an imaging system may not estimate the amount of blur for a point object in an image generated by the imaging system with a desired level of accuracy. For example, the point spread function may not take into account motion blur caused by movement of the objects relative to the imaging system or movement of the imaging system relative to the scene captured in the image. In some cases, the spatial structure of the motion blur may vary randomly in time in an unpredictable manner.
Consequently, deconvolving an image generated by the imaging system with this type of point spread function may not produce an image with the desired reduction in blur. Therefore, it would be desirable to have a method and apparatus that takes into account at least some of the issues discussed above as well as possibly other issues.